CFP last date
20 January 2025
Reseach Article

An Analysis of Fuzzy Clustering Methods

by Virender Kumar Malhotra, Harleen Kaur, M. Afshar Alam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 19
Year of Publication: 2014
Authors: Virender Kumar Malhotra, Harleen Kaur, M. Afshar Alam
10.5120/16497-6578

Virender Kumar Malhotra, Harleen Kaur, M. Afshar Alam . An Analysis of Fuzzy Clustering Methods. International Journal of Computer Applications. 94, 19 ( May 2014), 9-12. DOI=10.5120/16497-6578

@article{ 10.5120/16497-6578,
author = { Virender Kumar Malhotra, Harleen Kaur, M. Afshar Alam },
title = { An Analysis of Fuzzy Clustering Methods },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 19 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number19/16497-6578/ },
doi = { 10.5120/16497-6578 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:18:03.286650+05:30
%A Virender Kumar Malhotra
%A Harleen Kaur
%A M. Afshar Alam
%T An Analysis of Fuzzy Clustering Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 19
%P 9-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts through the use of membership functions, which allows membership with a certain degree. It has found application in numerous problem domains. It has been used in the interval [0, 1] fuzzy clustering, in pattern recognition and in other domains. In this paper, we introduce fuzzy logic, fuzzy clustering and an application and benefits. A case analysis has been done for various clustering algorithms in Fuzzy Clustering. It has been proved that some of the defined and available algorithms have difficulties at the borders in handling the challenges posed in collection of natural data. An analysis of two fuzzy clustering algorithms namely fuzzy c-means and Gustafson Kessel Fuzzy clustering Algorithm has been analyzed

References
  1. Kasabov, N. K. and Song, Q. 2002 “DENFIS: Dynamic Evolving Neural Fuzzy Inference system and its application for Time-Series Prediction” IEEE transactions on Fuzzy system, Vol. 10( 2), pp. 144-154.
  2. Jian Yu, Miin-Shen Yang. 2007. “A Generalized Fuzzy Clustering Regularization Model with Optimally Tests and Model Complexity Analysis” IEEE transactions on Fuzzy System Vol. 15(5), pp. 904-915.
  3. Sato, M. and Sato, Y. 1995. “Fuzzy clustering model for fuzzy data”, Fuzzy systems,1995, International Joint conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium Proceedings of 1995, IEEE International, Vol. 4, pp. 2123-2128.
  4. Bezdek, J. C. 1973. “Fuzzy Mathematics in Pattern classification” Ph.D. Thesis Centre for Applied Mathematics Cornell University, N.Y.
  5. Han, J., Kamber, M., 2006. "Data Mining: Concepts and Techniques, Second Edition” , Morgan Kaufmann.
  6. Klir G. J., Folger T. A., 1998. “Fuzzy sets, Uncertainty and information”, Prentice Hall.
  7. Francisco de A.T. de Carvalho, Camilo P. Tenorio. 2010. “Fuzzy K-means clustering algorithms for interval valued data based on adaptive quadratic distances”, Fuzzy Sets and Systems, Vol. 161(23), pp. 2978-2999.
  8. Chen, K.C.C, Au, W-H., Keith, Choi, B. 2002. “Mining Fuzzy rules in a Donor Database for Direct Mining by a charitable organization” Proceedings First IEEE international Conference on Cognitive Informatics, pp. 239-246.
  9. M.H. Fazel Zarandi, Zahara S. Razaee. 2010. “A Fuzzy Clustering Model for Fuzzy Data with Outliers”, International Journal of Fuzzy System Applications, Vol. 1( 2), IGI Global Publishers.
  10. Xiang Li, Hau-San Wong, Si. Wu. 2012. “A fuzzy minimax clustering model and its applications” Information Sciences: an International Journal, Vol. 186 (1), 114-125, Elsevier Science Inc..
  11. Pierpaolo D’Urso., Paolo Giordani. 2006. “A weighted fuzzy c-means clustering model for fuzzy data”, Computational Statistics & Data Analysis Vol. 50 (6), pp. 1496-1523, Elsevier Science Pub..
  12. Inmon, W.H. 1996. “The data warehouse and data mining”, Communications of ACM, Vol . 39 (11), pp. 49-50.
  13. M. Halkidi, D. Spinellis, G. Tsatsaronis, M. Vazirgiannis. 2011. “Data mining in software engineering”, Intelligent Data Analysis Journal, Vol. 15(3).
  14. Au, W-H, Chan, K.C.C. 2001. “Classification with Degree of Membership: A Fuzzy Approach” Proceedings IEEE International Conference on Data mining, (ICDM 2001), pp. 35-42.
  15. Kruse, R., Borgelt, C, Nauck, D. 1999. “Fuzzy Data Analysis Challenges and Perspective”, Fuzzy System Conference Proceedings, Vol. 3, pp. 1211-1216.
  16. Mitra, S., Pal, S.K., Mitra, P. 2002. “Data Mining in Soft Computing Framework : A Survey”, IEEE transactions on neural networks, Vol. 13(1), pp. 3-14.
  17. http://fuzziness.org/fcm
  18. Kaur, H., Chauhan, R., Wasan, S. K. 2014. “A Bayesian Network Model for Probabilistic Estimation”, Encyclopedia of Information Science and Technology, Third Edition, IGI Publishers, USA.
  19. Chauhan, R., Kaur, H. 2014. “Predictive Analytics and Data Mining: A framework for optimizing decisions with R tool”, Advances in Secure Computing, Internet Services, and Applications, 73-88, IGI Publishers, USA.
  20. Kaur, H., Chauhan, R., Aljunid ,S. 2012. “Data Mining Cluster analysis on the influence of health factors in Casemix data”, BMC Journal of Health Services Research, (June 2012). 12:O3
  21. Kaur, H., Chauhan, R., Alam, A. M. 2011. “Spatial Clustering Algorithm using R tree”, Journal of Computing, 3(2), pp. 85-90, 2011.
  22. Malhotra, V.K., Kaur, H., Alam A. M. 2013. “A Spectrum of Fuzzy Clustering Algorithms and its applications”, IEEE International Conference on Machine Intelligence, Research and Advancement (ICMIRA), pp. 599-603.
  23. Wasan, S. K., Bhatnagar, V., Kaur, H. 2007. “An Efficient Interestingness based Algorithm for Mining Association Rules in Medical Databases”, Advances in Systems, Computing Sciences and Software Engineering, Springer, pp. 167-172.
Index Terms

Computer Science
Information Sciences

Keywords

Component Fuzzy clustering Algorithms Fuzzy C-means Gustafson Kessel fuzzy clustering algorithm